Machine learning in the cloud
ML development is a complex and costly process. There are barriers to adoption at each step of the ML workflow, from collecting and preparing data, which is time-consuming and undifferentiated, to choosing the right ML algorithm, which is often done by trial and error, to lengthy training times, which leads to higher costs. Then there is model tuning, which can be a very long cycle and requires adjusting thousands of different combinations. Once you've deployed a model, you must monitor it and then scale and manage its production.
To solve these challenges, all major public cloud vendors provide an ML platform that facilitates ease of training, tuning, and deploying ML models anywhere at a low cost. For example, Amazon SageMaker is one of the most popular platforms that provides end-to-end ML services. SageMaker provides users with an integrated workbench of tools brought together in one place through SageMaker Studio. Users can launch Jupyter...
